Much research in artificial intelligence is concerned with the development of autonomous
agents that can interact effectively with other agents. An important aspect of such agents is …

In most challenging data analysis applications, data evolve over time and must be analyzed
in near real time. Patterns and relations in such data often evolve over time, thus, models …

Concept drift refers to a non stationary learning problem over time. The training and the
application data often mismatch in real life problems. In this report we present a context of …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

Designing coordinated robot behaviors in uncertain, dynamic, real-time, adversarial
environments, such as in robot soccer, is very challenging. In this work we present a case …

In today's information society, computer users are used to gathering and sharing data
anytime and anywhere. This concerns applications such as social networks, banking …

Due to the development of computing technology, various data are collected and analyzed
to extract meaningful information and are utilized in various industries. In particular, in the …

Understanding traffic conditions, in an urban environment, by means monitoring or/and
predicting is not an easy task. In Intelligent Transportation Systems, the reliable vehicle route …

Understanding the evolution of dynamical systems is an ILP topic with many application
domains, eg, multi-agent systems, robotics and systems biology. In this paper, we present a …

The problem of defining robot behaviors to completely address a large and complex set of
situations is very challenging. We present an approach for robot's action selection in the …